Machine Learning Interview Questions & Answers for US-Based Jobs in 2026

Demand for machine learning engineers in the US is expected to increase in 2026, as companies across various sectors adopt AI solutions. Interestingly, the global machine learning market was valued at $19.2 billion in 2022, with projections of reaching $225.9 billion by 2030, representing a 36.2% compound annual growth rate (CAGR). At the same time, data, science, and machine learning jobs may grow by around 36% between 2023 and 2030.

It is perhaps the best time to build a career in machine learning in the US. However, thoroughly preparing for interviews will enhance your chances of getting hired. Here are some common machine learning interview questions that you may come across in interviews.

Ace Your 2026 Interviews with These Machine Learning Interview Questions

Let us first look at a round-up of what you can expect at each difficulty level in terms of topics, so that you can ace your machine learning interview preparation.

Difficulty Level	Topics
Basic	Supervised and Unsupervised Learning Underfitting Overfitting Bias-Variance Tradeoff
Intermediate	Feature Engineering Model Evaluation Techniques Hyperparameter Tuning
Advanced	Generative Ai Deep Learning Reinforcement Learning

Also Read: Machine Learning with Python for Beginners in the US

Basic Machine Learning Interview Questions

Here are some of the questions that you may encounter in your machine learning mock interview.

• What is the difference between supervised and unsupervised learning?

Supervised learning uses labeled data to train models for predicting new outputs, where the desired output and input are known. Unsupervised learning uses unlabeled data to find structures, relationships, or patterns within the same without any particular guidance.

• What is Overfitting and Underfitting? 

Overfitting is when a model learns the training data too well, including noise, and performs poorly on new data. Underfitting occurs when a model fails to capture the underlying patterns, resulting in poor performance on both training and test data.

• What is the relationship between bias and variance? 

The bias-variance tradeoff is vital in machine learning. It means the inverse relationship between the variance and bias of a model. A higher bias may lead to lower variance and vice versa.

• Why is cross-validation important? 

Cross-validation ensures a more accurate estimate of model performance using unseen data and also helps combat overfitting. It involves testing the model on varying data subsets.

Intermediate-Level ML Questions

These are some intermediate-level ML engineer interview questions that you should prepare for:

• What are some feature engineering techniques? 

Some techniques include one-hot encoding, label encoding, and target encoding. Others include imputation, deletion, polynomial features, and feature scaling.

• How can you evaluate the effectiveness of feature engineering? 

One can evaluate whether the new features impact model performance. Metrics such as precision, recall, accuracy, F-1 score, and cross-validation techniques are relevant for this purpose.

• What are hyperparameters, and how are they different from model parameters? 

Hyperparameters are configuration settings fixed before training. Model parameters, on the other hand, are learned during the training.

• What are some techniques used for hyperparameter training? 

Some hyperparameter techniques include random search, grid search, and Bayesian optimization.

Advanced Machine Learning Questions

Let’s now consider a few advanced machine learning interview questions and their answers.

What are vanishing or exploding gradients in deep neural networks? 

Vanishing and exploding gradients are issues that can arise during the training of deep neural networks. These occur during backpropagation when gradients become too small (vanishing) or too large (exploding). Vanishing gradients slow down or stop learning, while exploding gradients cause unstable and divergent training.

What are the key reinforcement learning concepts- environment, agent, state, action, reward, and policy? 

The agent learns by interacting with the environment. A state represents the current situation, and an action is the agent’s response to it. A reward is the feedback received, and a policy is the agent’s strategy for choosing actions.

What are generative adversarial networks or GANs? 

GANs are deep learning models that create new data instances resembling existing data. They comprise two neural networks, namely a discriminator and a generator, which compete against each other.

Scenario-Based ML Interview Questions

You may be asked questions about using machine learning knowledge to solve real-world problems. Some examples include:

• A bank wishes to predict loan defaults based on customer data- which techniques will you consider? (Decision Tree, Logistic Regression, LightGBM, XGBoost)
• A retailer wants to recommend products to customers based on their purchasing history. What techniques will you use for building this system? (Matrix Factorization and Collaborative Filtering)

Behavioral & Soft Skill Questions for ML Roles

It’s not just about the technical questions; several interviewers also seek candidates who are good team players and have suitable communication and problem-solving skills. Here are some questions worth noting in this category.

• Describe when you worked together in a team to solve a problem
• How do you tackle conflicts within teams?
• Have you led any projects or teams, and how did you ensure that everyone was motivated?
• Talk about when you gave helpful feedback to a teammate that was actionable and clear
• Did you identify a problem before it turned critical? What steps did you take to address it?

Also Read: What is Gradient Boosting in Machine Learning?

Top Resources to Ace Your ML Interview

Here are some helpful resources to crack your ML interview successfully.

• Machine Learning Courses at upGrad
• The Hundred Page Machine Learning Book (Andriy Burkov)
• Advanced Techniques in NLP
• NLP MetaBlog by Pratik Bhavsar
• LLMs Overview by Maxime Labonne
• Github: Reinforcement Learning Overview
• Medium: A Complete Guide to Time-Series Forecasting
• Github: 100 Days of ML Code
• Kaggle
• AlgoExpert ML Coding Questions

Also Read: How to Learn Machine Learning Online in the US

Advance Your ML Career with upGrad’s AI & ML Programs

upGrad offers cutting-edge AI and ML programs that equip you with the skills and knowledge you need in an increasingly competitive digital ecosystem. You can expect affordable and flexible courses, along with certifications, expert insights, and dedicated support, for greater peace of mind.

Some popular Machine Learning AI programs available on upGrad:

• Master of Science in Machine Learning & AI
• Executive Diploma in Machine Learning and AI with IIIT-B

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